Interactive graphics with plotly
install.packages("plotly")
First Interactive Plot
library(tidyverse)
library(plotly)
p <- ggplot(data = midwest) +
geom_point(mapping = aes(x = popdensity, y = percollege))
ggplotly(p)
Customized Interactive Plot
p <- ggplot(midwest,
aes(x = popdensity, y = percollege, color = state)) +
geom_point() +
scale_x_continuous("Population Density",
breaks = seq(0, 80000, 20000)) +
scale_y_continuous("Percent College Graduates") +
scale_color_discrete("State") +
theme_bw()
ggplotly(p)
Your Turn
- Using the
starwars data, create a static ggplot and use the ggplotly function to turn it interactive.
Lord of the Rings Data
lotr <- read_tsv('https://raw.githubusercontent.com/jennybc/lotr/master/lotr_clean.tsv')
lotr
Create plotly by hand
plot_ly(lotr, x = ~Words) %>% add_histogram()
Subplots
one_plot <- function(d) {
plot_ly(d, x = ~Words) %>%
add_histogram() %>%
add_annotations(
~unique(Film), x = 0.5, y = 1,
xref = "paper", yref = "paper", showarrow = FALSE
)
}
lotr %>%
split(.$Film) %>%
lapply(one_plot) %>%
subplot(nrows = 1, shareX = TRUE, titleX = FALSE) %>%
hide_legend()
Grouped bar plot
plot_ly(lotr, x = ~Race, color = ~Film) %>% add_histogram()
Plot of proportions
## number of diamonds by cut and clarity (n)
lotr_count <- count(lotr, Race, Film)
## number of diamonds by cut (nn)
lotr_prop <- left_join(lotr_count, count(lotr_count, Race, wt = n),
by = 'Race')
lotr_prop %>%
mutate(prop = n.x / n.y) %>%
plot_ly(x = ~Race, y = ~prop, color = ~Film, width = 900) %>%
add_bars() %>%
layout(barmode = "stack")
Your Turn
- Using the
gss_cat data, create a histrogram for the tvhours variable.
- Using the
gss_cat data, create a bar chart showing the partyid variable by the marital status.
Scatterplots by Hand
plot_ly(midwest, x = ~popdensity, y = ~percollege) %>%
add_markers()
Change symbol
plot_ly(midwest, x = ~popdensity, y = ~percollege) %>%
add_markers(symbol = ~state)
Change color
plot_ly(midwest, x = ~popdensity, y = ~percollege) %>%
add_markers(color = ~state, colors = viridis::viridis(5))
Line Graph
storms_yearly <- storms %>%
group_by(year) %>%
summarise(num = length(unique(name)))
plot_ly(storms_yearly, x = ~year, y = ~num) %>%
add_lines()
Your Turn
- Using the
gss_cat data, create a scatterplot showing the age and tvhours variables.
- Compute the average time spent watching tv by year and marital status. Then, plot the average time spent watching tv by year and marital status.
Highcharter; Highcharts for R
devtools::install_github("jbkunst/highcharter")
hchart function
library(highcharter)
Highcharts (www.highcharts.com) is a Highsoft software product which is
not free for commercial and Governmental use
lotr_count <- lotr %>%
count(Film, Race)
hchart(lotr_count, "column", hcaes(x = Race, y = n, group = Film))
A second hchart
hchart(midwest, "scatter", hcaes(x = popdensity, y = percollege, group = state))
Histogram
hchart(lotr$Words)
Your Turn
- Using the
hchart function, create a bar chart or histogram with the gss_cat data.
- Using the
hchart function, create a scatterplot with the gss_cat data.
Build Highcharts from scratch
hc <- highchart() %>%
hc_xAxis(categories = lotr_count$Race) %>%
hc_add_series(name = 'The Fellowship Of The Ring',
data = filter(lotr_count, Film == 'The Fellowship Of The Ring')$n) %>%
hc_add_series(name = 'The Two Towers',
data = filter(lotr_count, Film == 'The Two Towers')$n) %>%
hc_add_series(name = 'The Return Of The King',
data = filter(lotr_count, Film == 'The Return Of The King')$n)
hc
Change Chart type
hc <- hc %>%
hc_chart(type = 'column')
hc
Change Colors
hc <- hc %>%
hc_colors(substr(viridis::viridis(3), 0, 7))
hc
Modify Axes
hc <- hc %>%
hc_xAxis(title = list(text = "Race")) %>%
hc_yAxis(title = list(text = "Number of Words Spoken"),
showLastLabel = FALSE)
hc
Add title, subtitle, move legend
hc <- hc %>%
hc_title(text = 'Number of Words Spoken in Lord of the Rings Films',
align = 'left') %>%
hc_subtitle(text = 'Broken down by <i>Film</i> and <b>Race</b>',
align = 'left') %>%
hc_legend(align = 'right', verticalAlign = 'top', layout = 'vertical',
x = 0, y = 80) %>%
hc_exporting(enabled = TRUE)
hc
Your Turn
- Build up a plot from scratch, getting the figure close to publication quality using the
gss_cat data.
Correlation Matrices
select(storms, wind, pressure, ts_diameter, hu_diameter) %>%
cor(use = "pairwise.complete.obs") %>%
hchart()
Leaflet Example
library(leaflet)
storms %>%
filter(name %in% c('Ike', 'Katrina'), year > 2000) %>%
leaflet() %>%
addTiles() %>%
addCircles(lng = ~long, lat = ~lat, popup = ~name, weight = 1,
radius = ~wind*1000)
---
title: "Data Visualization - Interactive Graphics using R"
author: "Brandon LeBeau"
date: "February 21, 2019"
output: html_notebook
---

```{r setup_chunks, echo = FALSE}
knitr::opts_chunk$set(fig.width=12, fig.height=6, fig.cap = NULL) 
```

# Interactive graphics with plotly
```{r install, eval = FALSE}
install.packages("plotly")
```

# First Interactive Plot
```{r first_plotly, message = FALSE, fig.width = 9, fig.height = 6}
library(tidyverse)
library(plotly)
p <- ggplot(data = midwest) +
  geom_point(mapping = aes(x = popdensity, y = percollege))
ggplotly(p)
```

# Customized Interactive Plot
```{r custom_plotly, warning = FALSE, fig.width = 9, fig.height = 6}
p <- ggplot(midwest, 
       aes(x = popdensity, y = percollege, color = state)) +
  geom_point() + 
  scale_x_continuous("Population Density", 
                     breaks = seq(0, 80000, 20000)) + 
  scale_y_continuous("Percent College Graduates") + 
  scale_color_discrete("State") + 
  theme_bw()
ggplotly(p)
```

# Your Turn
1. Using the `starwars` data, create a static ggplot and use the `ggplotly` function to turn it interactive. 

# Lord of the Rings Data
- Data from Jenny Bryan: <https://github.com/jennybc/lotr>

```{r read_in_lotr, error = FALSE}
lotr <- read_tsv('https://raw.githubusercontent.com/jennybc/lotr/master/lotr_clean.tsv')
lotr
```

# Create plotly by hand
```{r plotly_by_hand, fig.width = 9, fig.height = 6}
plot_ly(lotr, x = ~Words) %>% add_histogram()
```

# Subplots
```{r subplots, fig.width = 9, fig.height = 6}
one_plot <- function(d) {
  plot_ly(d, x = ~Words) %>%
    add_histogram() %>%
    add_annotations(
      ~unique(Film), x = 0.5, y = 1, 
      xref = "paper", yref = "paper", showarrow = FALSE
    )
}

lotr %>%
  split(.$Film) %>%
  lapply(one_plot) %>% 
  subplot(nrows = 1, shareX = TRUE, titleX = FALSE) %>%
  hide_legend()
```


# Grouped bar plot
```{r plotly_group, fig.width = 9, fig.height = 6}
plot_ly(lotr, x = ~Race, color = ~Film) %>% add_histogram()
```

# Plot of proportions
```{r plotly_proportions, message = FALSE, fig.width = 9, fig.height = 6}
## number of diamonds by cut and clarity (n)
lotr_count <- count(lotr, Race, Film)
## number of diamonds by cut (nn)
lotr_prop <- left_join(lotr_count, count(lotr_count, Race, wt = n), 
                       by = 'Race')

lotr_prop %>%
  mutate(prop = n.x / n.y) %>%
  plot_ly(x = ~Race, y = ~prop, color = ~Film, width = 900) %>%
  add_bars() %>%
  layout(barmode = "stack")
```

# Your Turn
1. Using the `gss_cat` data, create a histrogram for the `tvhours` variable. 
2. Using the `gss_cat` data, create a bar chart showing the `partyid` variable by the `marital` status.

# Scatterplots by Hand
```{r plotly_scatter, fig.width = 9, fig.height = 6, warning = FALSE}
plot_ly(midwest, x = ~popdensity, y = ~percollege) %>%
  add_markers()
```

# Change symbol
```{r plotly_symbol, fig.width = 9, fig.height = 6}
plot_ly(midwest, x = ~popdensity, y = ~percollege) %>%
  add_markers(symbol = ~state)
```

# Change color
```{r plotly_color, fig.height = 6, fig.width = 9}
plot_ly(midwest, x = ~popdensity, y = ~percollege) %>%
  add_markers(color = ~state, colors = viridis::viridis(5))
```

# Line Graph
```{r plotly_line, fig.height = 6, fig.width = 9}
storms_yearly <- storms %>%
  group_by(year) %>%
  summarise(num = length(unique(name)))

plot_ly(storms_yearly, x = ~year, y = ~num) %>%
  add_lines()
```

# Your Turn
1. Using the `gss_cat` data, create a scatterplot showing the `age` and `tvhours` variables.
2. Compute the average time spent watching tv by year and marital status. Then, plot the average time spent watching tv by year and marital status.

# Highcharter; Highcharts for R
```{r install_highcharter, eval = FALSE}
devtools::install_github("jbkunst/highcharter")
```

# `hchart` function
```{r hchart1, fig.height = 6, fig.width = 9}
library(highcharter)

lotr_count <- lotr %>%
  count(Film, Race)

hchart(lotr_count, "column", hcaes(x = Race, y = n, group = Film))
```

# A second `hchart`
```{r hchart2, fig.height = 6, fig.width = 9}
hchart(midwest, "scatter", hcaes(x = popdensity, y = percollege, group = state))
```

# Histogram
```{r hchart_hist, fig.height = 6, fig.width = 9}
hchart(lotr$Words)
```

# Your Turn
1. Using the `hchart` function, create a bar chart or histogram with the `gss_cat` data.
2. Using the `hchart` function, create a scatterplot with the `gss_cat` data.

# Build Highcharts from scratch
```{r hc_scratch, fig.width = 9, fig.height = 6}
hc <- highchart() %>%
  hc_xAxis(categories = lotr_count$Race) %>%
  hc_add_series(name = 'The Fellowship Of The Ring', 
                data = filter(lotr_count, Film == 'The Fellowship Of The Ring')$n) %>% 
  hc_add_series(name = 'The Two Towers', 
                data = filter(lotr_count, Film == 'The Two Towers')$n) %>%
  hc_add_series(name = 'The Return Of The King', 
                data = filter(lotr_count, Film == 'The Return Of The King')$n)
hc
```

# Change Chart type
```{r hc_chart, fig.width = 9, fig.height = 6}
hc <- hc %>%
  hc_chart(type = 'column')
hc
```

# Change Colors
```{r hc_change_colors, fig.width = 9, fig.height = 6}
hc <- hc %>%
  hc_colors(substr(viridis::viridis(3), 0, 7))
hc
```

# Modify Axes
```{r hc_axis, fig.height = 6, fig.width = 9}
hc <- hc %>%
  hc_xAxis(title = list(text = "Race")) %>%
  hc_yAxis(title = list(text = "Number of Words Spoken"),
           showLastLabel = FALSE)
hc
```

# Add title, subtitle, move legend
```{r hc_modify, fig.height = 6, fig.width = 9}
hc <- hc %>%
  hc_title(text = 'Number of Words Spoken in Lord of the Rings Films',
           align = 'left') %>%
  hc_subtitle(text = 'Broken down by <i>Film</i> and <b>Race</b>', 
              align = 'left') %>%
  hc_legend(align = 'right', verticalAlign = 'top', layout = 'vertical',
            x = 0, y = 80) %>%
  hc_exporting(enabled = TRUE)
hc
```


# Your Turn
1. Build up a plot from scratch, getting the figure close to publication quality using the `gss_cat` data.

# Correlation Matrices
```{r correlation}
select(storms, wind, pressure, ts_diameter, hu_diameter) %>%
  cor(use = "pairwise.complete.obs") %>%
  hchart()
```

# Leaflet Example
```{r leaflet}
library(leaflet)

storms %>%
  filter(name %in% c('Ike', 'Katrina'), year > 2000) %>%
  leaflet() %>%
  addTiles() %>%
  addCircles(lng = ~long, lat = ~lat, popup = ~name, weight = 1,
             radius = ~wind*1000)
```


# Additional Resources
* plotly for R book: <https://plotly-book.cpsievert.me/>
* plotly: <https://plot.ly/>
* highcharter: <http://jkunst.com/highcharter/index.html>
* highcharts: <https://www.highcharts.com/>
* htmlwidgets: <https://www.htmlwidgets.org/>
